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      <title>分布式缓存的原理 - 交互式测验</title>
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    </head>
    <body>
      <div class="main-wrapper">
          <div class="quiz-container">
            <div class="quiz-header">
              <h1>分布式缓存的原理 - 交互式测验</h1>
              <p>检验你对文档内容的理解程度</p>
            </div>
            <div id="quiz-content">
                <div class="quiz-body">
                    <div class="progress-bar">
                        <div id="progress-indicator"></div>
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      </div>

      <script>
        const quizData = [
  {
    "question": "根据文档描述，当大量缓存数据在同一时间失效，导致所有请求瞬间涌向数据库，可能引发系统崩溃的现象被称为？",
    "options": [
      "缓存穿透 (Cache Penetration)",
      "缓存雪崩 (Cache Avalanche)",
      "缓存击穿 (Cache Breakdown)",
      "数据不一致 (Data Inconsistency)"
    ],
    "correctAnswerIndex": 1,
    "explanation": "缓存雪崩指的是大量缓存数据在同一时间点过期，导致海量请求直接访问数据库，从而对数据库造成巨大压力甚至导致其宕机。这与缓存击穿（单个热点key失效）和缓存穿透（请求不存在的数据）的定义不同。",
    "source": "文档中「分布式缓存的常见问题」部分的「2. 缓存雪崩」",
    "hint": "这个问题描述的是一个“群体性”的缓存失效事件，而非单个“热点”或“不存在”的数据问题。"
  },
  {
    "question": "文档中提到，为了防止请求查询一个既不存在于缓存也不存在于数据库中的数据（即缓存穿透），推荐的解决方案是什么？",
    "options": [
      "为缓存设置随机化的过期时间",
      "在缓存失效时使用互斥锁",
      "使用布隆过滤器或缓存空对象",
      "采用延迟写入（Write-behind）策略"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档明确指出，缓存穿透的解决方案包括使用布隆过滤器来预先判断key是否存在，或者将查询不到的结果（空对象）也缓存起来，并设置一个较短的过期时间，从而避免后续相同的无效请求继续穿透到数据库。",
    "source": "文档中「分布式缓存的常见问题」部分的「3. 缓存穿透」",
    "hint": "思考一下，如何在请求到达数据库之前，就能高效地判断一个“可能不存在”的数据。"
  },
  {
    "question": "在分布式缓存的更新策略中，哪种策略在写入数据时会立即同步更新缓存和数据库，从而保证了强一致性，但代价是较高的写入延迟？",
    "options": [
      "延迟写入 (Write-behind)",
      "缓存预热 (Cache preheating)",
      "写入时更新 (Write-through)",
      "LRU (Least Recently Used)"
    ],
    "correctAnswerIndex": 2,
    "explanation": "写入时更新（Write-through）策略的核心思想是，写操作会同时作用于缓存和后端数据库。这确保了数据的一致性，因为缓存和数据库总是同步的。然而，因为每次写操作都需要等待两个存储系统都完成，所以会增加写入操作的延迟。",
    "source": "文档中「分布式缓存的架构设计」部分的「2.2 缓存更新策略」",
    "hint": "这个策略的关键词是“同步”，它优先考虑的是数据的一致性而非写入性能。"
  },
  {
    "question": "文档对比了多种分布式缓存技术。相较于Memcached，Redis的一个显著优势是什么？",
    "options": [
      "只支持简单的键值对存储，设计更简洁",
      "不支持数据持久化，重启后数据全部丢失",
      "拥有更丰富的数据结构（如哈希、列表、集合）和持久化能力",
      "完全依赖客户端实现分布式"
    ],
    "correctAnswerIndex": 2,
    "explanation": "文档中明确提到，Redis支持包括字符串、哈希、列表、集合、有序集合在内的丰富数据结构，并提供RDB和AOF两种持久化方式。而Memcached主要提供简单的键值对存储，且不具备持久化机制。",
    "source": "文档中「常见的分布式缓存技术」部分的「1. Redis」和「2. Memcached」",
    "hint": "想一想，哪种技术不仅仅是一个简单的缓存，更像一个“数据结构服务器”。"
  },
  {
    "question": "当某个访问极其频繁的热点数据缓存过期时，大量并发请求同时涌入数据库查询并回填缓存，这种情况被称为？",
    "options": [
      "缓存雪崩",
      "缓存预热",
      "缓存穿透",
      "缓存击穿"
    ],
    "correctAnswerIndex": 3,
    "explanation": "缓存击穿特指单个热点Key在高并发场景下突然失效，导致所有对该Key的并发请求都直接打到数据库上。它与缓存雪崩（大量Key同时失效）的关键区别在于影响范围是“单个热点”而非“大量普遍”。",
    "source": "文档中「分布式缓存的常见问题」部分的「4. 缓存击穿」",
    "hint": "这个问题聚焦于单个“明星”或“热点”数据失效带来的冲击。"
  },
  {
    "question": "在分布式缓存的数据分布策略中，为什么一致性哈希算法被广泛采用？",
    "options": [
      "它能保证缓存与数据库的强一致性",
      "当集群中增删节点时，能最小化需要迁移的数据量",
      "它要求所有节点都存储一份完整的数据副本",
      "它能彻底解决缓存击穿问题"
    ],
    "correctAnswerIndex": 1,
    "explanation": "一致性哈希算法的核心优势在于其平滑的扩展性。当缓存集群需要增加或移除节点时，它只会影响到哈希环上相邻的一小部分数据，需要重新映射（迁移）的数据量非常小，从而最大限度地减少了对整个缓存系统的扰动。",
    "source": "文档中「分布式缓存的工作原理」部分的「2.1 数据分布与分片」",
    "hint": "这项技术的关键优势体现在集群规模发生变化（例如扩容或缩容）时。"
  },
  {
    "question": "当缓存空间已满时，需要移除一些数据。如果系统选择移除“最久未被使用”的数据，这遵循的是哪种缓存淘汰策略？",
    "options": [
      "LFU (Least Frequently Used)",
      "TTL (Time-to-Live)",
      "FIFO (First-In, First-Out)",
      "LRU (Least Recently Used)"
    ],
    "correctAnswerIndex": 3,
    "explanation": "LRU (Least Recently Used) 策略的原则是，当缓存需要淘汰数据时，优先淘汰掉最近最少被访问的数据。它假设最近被访问的数据在将来也更有可能被再次访问。这与LFU（淘汰访问频率最低的）和TTL（基于过期时间）的策略不同。",
    "source": "文档中「分布式缓存的架构设计」部分的「2.2 缓存更新策略」",
    "hint": "这个策略的决策依据是数据最后一次被访问的“时间点”。"
  }
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            document.querySelector('.quiz-body').style.display = 'block';
            document.getElementById('quiz-footer').style.display = 'flex';
            
            currentQuestionIndex = 0;
            loadQuestion();
            updateNavigationButtons(); // Ensure nav buttons are correctly displayed
        }

        // Event Delegation for options
        optionsListEl.addEventListener('click', (event) => {
            if (event.target && event.target.matches('li.option-item')) {
                // Check if the question has already been answered
                if (userAnswers[currentQuestionIndex] === null) {
                    const selectedIndex = parseInt(event.target.dataset.index, 10);
                    selectOption(selectedIndex);
                }
            }
        });

        prevBtn.addEventListener('click', goToPreviousQuestion);
        nextBtn.addEventListener('click', goToNextQuestion);
        hintBtn.addEventListener('click', showHint);
        
        // 初始加载
        loadQuestion();
      </script>
    </body>
    </html>
  